South Asia Multidisciplinary Academic Journal Free-Standing Articles | 2018 Seeking the Indian Social Space A Multidimensional Portrait of the Stratifications of Indian Society Mathieu Ferry, Jules Naudet and Olivier Roueff Electronic version URL: http://journals.openedition.org/samaj/4462 DOI: 10.4000/samaj.4462 ISSN: 1960-6060 Publisher Association pour la recherche sur l'Asie du Sud (ARAS) Electronic reference Mathieu Ferry, Jules Naudet and Olivier Roueff, « Seeking the Indian Social Space », South Asia Multidisciplinary Academic Journal [Online], Free-Standing Articles, Online since 20 February 2018, connection on 19 April 2019. URL : http://journals.openedition.org/samaj/4462 ; DOI : 10.4000/ samaj.4462 This text was automatically generated on 19 April 2019. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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South Asia Multidisciplinary AcademicJournal Free-Standing Articles | 2018
Seeking the Indian Social SpaceA Multidimensional Portrait of the Stratifications of Indian Society
PublisherAssociation pour la recherche sur l'Asie du Sud (ARAS)
Electronic referenceMathieu Ferry, Jules Naudet and Olivier Roueff, « Seeking the Indian Social Space », South AsiaMultidisciplinary Academic Journal [Online], Free-Standing Articles, Online since 20 February 2018,connection on 19 April 2019. URL : http://journals.openedition.org/samaj/4462 ; DOI : 10.4000/samaj.4462
This text was automatically generated on 19 April 2019.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0International License.
Figure 1 – Unstandardized Factor analysis (PCA) of the Indian social space. Plane 1-2
Note: The representation of planes 1-2 shows three coherent geographical groups. Axis 1is correlated with the standard of living (MPCE) whereas Axis 2 is correlated withownership of land.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round (2011-2012)
27 The projection of the “State” variable on the first and second axes of the PCA indeed
reveals three coherent groups: the states of the north and the center (at the bottom of the
figure, and spread along the first axis), associated with the possession of large landed
property, the states of the south (at the top right of the figure) and the states of the
north-east (spread along a diagonal going from the top left of the figure towards the
bottom right). The weight of regional disparities is hence a determining factor in the
explanation of modes of consumption.
28 These three geographical clusters correspond to the well-documented divide between
regions on the basis of language (respectively Indo-European, Dravidian and Tibeto-
Burman), economics, culture and climate. But the identification of a geographical effect is
not really heuristic as such. The consumption of certain food goods may be linked to the
geographical location of the respondent, which is itself dependent on climatic conditions,
or linked to price variations between consumer goods, depending on the State. The
geographical variable may also conceal other effects that structure the social space, such
as standards of living, the caste system, or urbanization. In this latter hypothesis, the
geographical structure actually masks the social structure that we seek to reveal. In order
to avoid such an unfortunate bias, the fourth step of our analysis thus consists in
neutralizing the “State” variable in order to recalculate correlations “free” of this
geographical effect.9
29 To do this we use the Standardized factor analysis method (SFA) that neutralizes the
effect of a variable on the construction of the factor axes of a geometric analysis, in order
to study structural effects (Bry, Robette and Roueff 2015). In metaphorical words (see
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Appendix 1 for a detailed presentation), this algorithm acts as if one had forced all
modalities of the “State” variable to be located at the crossing of the axes of our two-
planes figures (i.e., the coordinates “0,0”), just as if its effect were null. It then
reorganizes all other data (the points representing each individual as well as each
category of active and supplementary variables except “State”) depending on this
constraint. This manner of neutralizing one of the main indicator of heterogeneity in the
Indian social space (variations by State) directly illustrates the pragmatic nature of our
approach: it does not intend to demonstrate the unity of the Indian social space at the
national scale (not finding the variations we have neutralized can obviously not
constitute an empirical proof of unity!) but it’s rather focused on figuring out what kind
of knowledge can be produced by carrying out an inductive analysis based on the
hypothesis of unity. The analysis of the key polarizing factors of this Standardized PCA (to
which we will refer as “SFA”) is presented in this article.
30 In the same rationale, we also analyzed the structural effects revealed by “zooming” our
SFA on subsamples, in order to compare the pan-Indian social space with rural and urban
spaces, and richest, intermediate, and poorest households’ spaces. The main result is that
each of these spaces is very similar to the others—even if some minor variations are very
heuristic (see Appendix 2 for details and results). This methodological detour helped us
be confident in the statistical validity of the standardized factor space.
31 The last step of our analysis finally consisted in developing a synthetic description of the
fractions of the Indian social space by conducting an Ascending Hierarchical
Classification (AHC), based on the previous standardized factor step. This AHC allows us
to construct a rich typology of the Indian social space that distinguishes nine fractions of
classes with contrasting consumer profiles. Rather than corresponding to a mere scale of
economic capital, each of them corresponds to a combination of social properties (total
level of consumption, as well as the area they live in, caste, qualification and property
ownership). This allows us to construct a statistically substantiated representation of the
Indian social space. The discussion of this representation actually constitutes the core of
this article.
Structuring principles and fractions of the Indiansocial space
The weight of need: the structuring principles of the
Indian social space
32 Having statistically neutralized the effect of regional variations, our analysis consists in
drawing upon the SFA (see figure 2 below) in order to explore the key polarizing factors
in Indian society. The statistical method adopted allows us to reveal the massive weight of
the level of wealth and the specific effects of secondary principles such as caste or living
in a rural or an urban area. The overall outcome of the standardized factor analysis is
unambiguous. The distribution of consumption follows two correlated but distinct
rationales (figure 2, also see Appendix 1 for a detailed presentation of the contribution of
active and supplementary variables).
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Note: In red, the consumption level; yellow crosses: the other position variables(specifically: level of education, professional category, religion, Scheduled Caste,demographic density, land ownership, area of residence); in blue: proportion of thebudget dedicated to each good; orange dots: the level of expenditure for each good.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
33 On axis 1 (horizontal), most of the goods owned (orange dots) are located on the right and
most of the budgetary coefficients (blue dots) are located on the left. This axis thus sums
up an opposition between budgetary structures in which a large share is reserved for
subsistence goods (food, in particular, but also basic energy, basic clothing) and
budgetary structures in which a large share is dedicated to comfort goods (computer,
refrigerator, land line telephones, air conditioning and fan, expenditure on education,
leisure, transportation, etc.).
34 As for axis 2, it shows consumption structures related to rural lifestyles (at the bottom)
and consumption structures associated with urban lifestyles (at the top). Thus in
subsistence consumption, a rural variation (dung cake,10 non-subsidized sugar and
cereals) can be distinguished from an urban variation (other energy sources, subsidized
sugar and cereals11). Similarly, some comfort consumption categories are more urban
(commercial leisure activities, packaged drinks, served meals, fruits)—but few seem to be
specifically rural.
35 This interpretation is confirmed when we observe the variables of social position
projected onto the same space as supplementary variables.
36 Axis 1 (horizontal) is very clearly principally associated with social position variables: the
level of consumption, the professional category, the level of education and caste, with the
social scale “rising” from left to right. More precisely, on the right we find farmer
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owners, higher and lower professionals, and on the left, subaltern agricultural and
industrial workers. Axis 2 (vertical) contrasts a more urban world of work (higher and
lower professionals) with a more rural working universe (farmers, subaltern agricultural
and industrial workers).
37 Finally, the diagonal along which the consumption variables, and moreover, the social
position variables are aligned, express the structuring power of the level of consumption
(Monthly Per Capita Expenditure or MPCE), associated with the level of education and
caste, and secondarily with the sector of residence—although the latter has a specific
weight, it is nonetheless clear that the rich are more commonly found in cities. It is also
to be noticed that the results obtained after zooming on subsamples distinguishing
respondents living in rural and urban environments, or the richest, intermediate and
poorest households, further show that these different sub-spaces are overall very similar
to each other and to the global space (see Appendix 2 for a detailed analysis of the minor
but nonetheless heuristic differences that appear on these subspaces).
38 The SFA shows different position variables—professional category, caste, level of
education—aligned with the level of consumption (see Figure 2). From this, one could
deduce that the latter is a sufficient indicator of the social position or, in other words,
that the social scale can be reduced to an opposition between rich and poor. Now, as
suggested above, this is not the case: the different position variables are certainly
correlated, but not strictly redundant. The case of caste is a good illustration of this.12
39 On the one hand, caste is strongly associated with the professional category. The
scheduled tribes (STs) and scheduled castes (SCs) are over-represented among the daily
agricultural workers and the unqualified or little qualified workers, while the upper
castes are very highly over-represented among the higher and lower professionals. The
OBCs are, for their part, represented in a relatively balanced manner over all the
professional categories, although they are under-represented among the higher
categories but over-represented among petty shopkeepers. On the other hand, a closer
look at what is happening at the “top” and the “bottom” of the social space shows that a
linear association between caste and class is not necessarily the rule (see Appendix 2 for
further details). A zoom on the sub-space of the poorest indeed show that the small
differences between the poor are more strongly based on caste than on education or the
professional category. Among the populations that possess no economic or cultural
capital, the caste one belongs to would hence make an enormous difference in terms of
creating a minimum distance from the weight of need, much more so than social class or
education.
40 This last remark encourages us to take into account the secondary differentiating factors
that “complicate” the massive effect of the level of consumption. Beyond an arbitrary
division into two (a two-tier India) or three (the classic upper, middle and lower classes),
is it possible to distinguish a typology of the Indian social space that simultaneously
reveals the dizzying length of the social scale, its twists dependent on the combination of
varying social power relationships (wealth, profession, education, caste) and the
multiplicity of its successive levels? The production of an ascending hierarchical
classification on the standardized PCA turns out to be particularly convincing in order to
further explore the multidimensionality of the Indian social space.
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41 As we’ve seen, the exploration of the structuring principles of the Indian social space
leads us to note an interaction between the massive weight of economic capital and the
secondary effects of other power relationships like the level of education, professional
category, caste and the rate of urbanization. To study this interaction further, we use an
Ascending Hierarchical Classification (AHC) carried out on the “standardized” space of
consumption. By segmenting the population on the basis of the relative commonality of
the same consumer practices, we expect to obtain groups that have sociologically
coherent social properties. The statistics of the semi-partial R squared show thresholds at 3,
7 and 9 clusters. The segmentation into 3 is rejected as one of the clusters groups
95 percent of the sample. The segmentation into 7 is more relevant, but has the drawback
of combining two diametrically opposed sociological profiles—the richest and the mass of
popular classes—within the most numerically important cluster (48.3 percent of the
sample). The classification combines them as they share a higher rate of non-responses.
In fact, the segmentation into 9 separates these two profiles into two distinct clusters.
42 The cluster analysis is based on the modalities of the consumption and position variables
that are over and under-represented within each cluster. We thus obtain the most typical
modalities of each cluster. It is all the more necessary to take the precautions usually
applicable to any classification, as the inevitable statistical “noise”—that is to say the
people in the sample whose qualities are badly captured by the segmentation—are
incorporated into the clusters here. Such classifications often produce a larger or smaller
cluster, which condenses the main residues that cannot be reduced to any factor of
classification. Here, this cluster, the 9th, turns out to be very small (0.69 percent of the
sample) and, moreover, it is relatively interpretable (we will return to this). In contrast,
each of the other clusters shows a sociologically coherent majority profile while also
remaining relatively heterogeneous: each cluster consists of a non-negligible proportion
of aberrant individuals from the viewpoint of the dominant profile they appear alongside.
Thus the segmentation we present should not be read as a strict representation of the
classes and fractions of classes that make up the Indian social space. It is a typology of
polarities that structure this space: the consumption and position profiles around which
cohesive circles of individuals aggregate are surrounded by wide, less homogenous
fringes with borders that overlap between profiles. It is this polarized rather than
segmented social space that Figure 4 (see below) attempts to represent in a synthetic
manner, the percentage size of each cluster being more an indication of the relative
importance of each profile rather than an accurate quantification of a social group.
43 The MPCE variable that contributes the most to the construction of the space clearly
organizes the nine clusters. Figure 3 also shows that the relative dispersion of each
cluster around the medians (bold lines separating the white rectangles from the second
and third quartiles) reduces linearly with the level of expenditure, with the exception of
cluster 5, which we will return to below. The size of the third quartiles (upper white
rectangles), the position of the averages (diamonds) and the position of the extreme
individuals in the fourth quartiles (stacking of bold dots at the apex of the graph) show
that this heterogeneity is mainly due to the relative presence of very high levels of
expenditure within each cluster.
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Figure 3 – Box plots of the levels of consumption for the nine clustersof the social space
Note: Each box plot shows the median of the distribution of consumption (boldhorizontal line), surrounded by the second quartile (below) and the third quartile(above).
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
44 This arrangement by level of consumption (Table 1) reveals three groups: 3 fractions of
the popular classes (clusters 1, 2 and 3), 2 fractions of the intermediate classes (clusters 4
and 5) and 3 fractions of the affluent classes (clusters 6, 7 and 8) to which we will add the
upper class profile of cluster 9.
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Table 1 – Synthetic summary of the clusters in the Indian social space
Figure 4 – Synthetic diagram of the Indian social space
Legend: The clusters are positioned on the vertical scale of the level of consumption (MPCE)depending on their median, and on the horizontal rural/urban axis according to the share of urban andrural. Their width corresponds to the percentage of the sample represented by each of them (theheight is merely half the width). The vignettes show the name of the cluster and its number inbrackets, the under-represented modalities with a strikethrough, the over-represented social positionin italics, the distribution of reserved groups (castes)—scheduled tribes (ST), scheduled castes (SC), other backward classes (OBC), forward castes (FC).
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The popular classes: 59 percent of the Indian population
45 Extreme deprivation. Cluster 1 gathers the most deprived households and represents no
less than 10 percent of the population. It is characterized by consumption structures
subject to the most compelling need, thus reduced to basic food, particularly subsidized
products, and does not even include access to cheap lentils (khesari) or the most common
energy source in rural zones (dung cake). The modal properties of the cluster agglomerate
the most vulnerable categories present in rural zones: agricultural workers or small
farmers, workers and unqualified employees, small informal businesses with a clear over-
representation of STs, SCs as well as religious minorities.
46 Popular classes. A comparison of cluster 1 with cluster 2 confirms the importance of micro-
resources among the poorest, which we had already noticed thanks to the PCA: caste
status and network and/or relative job stability enable the individual to escape at least
the most extreme deprivation. Indeed, cluster 2, the largest with 40 percent of the
population, embodies the most common profile in India, that of severe poverty (the
second lowest level of expenditure) in rural and suburban zones. The modal consumption
is marked by constraints, although it escapes the most extreme need: it is defined, in a
way, in opposition to rich and urban consumption (under-representation of expenditure
on leisure and education as well as jewelry) as well as to the most deprived level of
consumption (access to khesari, dung cake, food and basic footwear, under-representation
of subsidized food products). With 40 percent of the population, the spectrum of social
properties is relatively wide. It is nonetheless clearly located “below” the better endowed
groups in the social space and just “above” the most deprived: it is characterized by
illiterate and low-level qualifications, agricultural workers and small farmers, workers
and unqualified employees, as well as owners of land of all sizes (an agricultural holding
may be large but infertile) and owners of their homes. Members of all religions and castes
belong to this cluster, but forward Hindu castes are underrepresented whereas OBCs are
slightly overrepresented.
47 Stable popular classes. Cluster 3 is relatively poor but escapes the most compelling need.
With 9 percent of the population, it is concentrated around subsistence consumption,
including subsidized food, as well as small comfort expenditure—particularly everyday
intoxicants (tobacco, betel, etc.), spices, animal food products. In terms of social
properties, it is difficult to distinguish it clearly from cluster 2. But as Cluster 3 is
associated with average density areas, we interpret it as a fraction of the popular classes
who have achieved stability through access to small resources and jobs in areas of
subaltern urbanization.
48 As the positioning of the poverty threshold in Figure 4 shows, the three profiles described
above: extreme deprivation, severe ordinary poverty and stabilized positions in the
popular classes, in themselves justify the utility of a multidimensional approach. Focusing
only on the economic threshold of poverty and the debates on the methods used to
calculate it, obscures the amplitude of the social groups whose experience is reduced to
fulfilling basic needs, while micro-resources actually create significant differences among
this 59 percent of the Indian population. These micro-resources refer to caste status,
home production, job status, and access to infrastructure depending on the area of
residence.
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The intermediate classes: 16 percent of the Indian population
49 Urban intermediate class. Cluster 4 represents 13 percent of the population and marks a
clearer distance from need—the modalities here represent the urban intermediate class
that is grouped around lower professionals. Small comfort consumption includes petrol
for motorized travel, purchase of fruits and packaged drinks, entertainment, toiletries,
pan (everyday intoxicants, more expensive than tobacco or betel), packaged food, which
is also to be seen in contrast to the served food of the richer urban inhabitants. In terms
of social properties, we note the absence of the ration card, the frequency of lower
professionals and average level qualifications, the stronger presence of forward castes
and the weaker presence of lower castes.
50 Upper caste intermediate class. Cluster 5 only represents 3 percent of the population. It
differs from the previous cluster mainly by the association between the frequency of
specifically Hindu upper castes, high-level qualifications, higher professionals and
ownership of large landed property. It is difficult to situate on the rural/urban axis, as
average densities are over-represented. Nonetheless, the modal consumption structure
shows an urban and cultivated lifestyle, suggesting a concern with distinction:
entertainment, leather shoes (a status symbol among professional male middle or higher
level executives), use of commercial services (tailor, hairdresser, household employees,
etc.), clothing, and reading. This association between an urban lifestyle, average densities
and ownership of property led us to position the cluster between the rural and the urban
in Figure 4.
The affluent classes: 25 percent of the Indian population
51 The cultural affluent class. Cluster 6 gathers 12 percent of the population around a
cultivated profile: urban, most often qualified, forward caste—and not necessarily the
richest nor the largest number of owners, but very often professionals or business people.
With their highly distinctive expenditure on reading, education and entertainment, this
profile adds motor fuel, leather shoes, and industrial milk products. Finally, this cluster
resembles the previous one by its lifestyle concerned with distinction, its qualification
level and the frequency of upper castes; it differs by a noticeable divergence in levels of
expenditure, which makes it a profile of the cultural bourgeoisie in the historical sense of
the term (urban and entrepreneur, shopkeeper or liberal profession, rather than
landowner).
Table 2 – The nine clusters by level of qualification
Note: In cluster 6, 19 percent belong to a household in which the referenceperson has the highest level of education, as compared with 17.5 percent incluster 7.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
52 In passing, we also find traces of an analytical element that we should be able to include
in the survey using more refined caste indicators: not only is there a correlation between
caste and class, but more specifically, on the one hand, in general there is a connection
between caste and cultural capital and, on the other hand, a differentiation between
upper economic castes (businessmen in the city, landowners in rural areas) and cultural
upper castes (more often upper professionals or liberal professions).
53 The urban affluent class and the agrarian affluent class. Cluster 7 comprises 8 percent of the
population and embodies the urban economic affluent class: comfort consumption
associated with urban life (access to healthcare and travel, home rental, served meals and
packaged beverages) in contrast to poor (dung cake) or rich rural consumption (related to
the domestic space: interior furnishing, kitchen utensils as well as jewelry). In terms of
properties, this profile combines professionals, qualification, distance from caste and
religious minorities.
54 Cluster 8 (4 percent of the population) is the agrarian variant of the former, in the sense
that it is spread over less dense urban areas and rural areas of average density (neither
rural deserts, nor areas of subaltern urbanization). This echoes a result produced by the
SFA on the richest 20 percent of the sample: axis 2 contrasted the highest urban densities
with the highest rural densities and not the lowest, as was the case on the other global and
partial SFAs (see Appendix 2). Richer than the former, particularly because they are more
often landowners, the members of this clusters are more likely to be from an upper Hindu
caste as well as to be professionals. In terms of consumption, these are people who invest
highly in the domestic space (furnishings, home entertainment equipment) as well as in
jewelry, healthcare and entertainment activities. This is obviously in complete opposition
to budgets centered on poor rural consumption patterns.
55 The upper class. Cluster 9 is more difficult to interpret as it only represents 0.69 percent of
the population and one of its principles of statistical aggregation is the high level of non-
response to questions about the status of the residence, salaried status and profession.
However, several indicators orient the interpretation towards the specific profile of an
upper class: an average level of expenditure far above the others and high-level
qualifications as well as residence in the densest urban areas (megapolises); elite
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Note: Budgetary items are used in the factor analysis as active variables. Theconsumption of cereals represents on average 12 percent of the total consumption. Thecontributions of the budgetary items for the first two factors of the PCA are presented,along with the coordinates of the SFA (“global” analyses).
Table A2 – Supplementary variables (1): Ownership of durable goodsand use of AYUSH treatments
ModalityDistribution
(%)
Axis 1 Axis 2
Coord.
PCA
Coord.
SFA
Coord.
PCA
Coord.
SFA
Possessed
items
Ayush Yes 30.14 0.12 0.08 -0.07 0.03
No 69.85 -0.05 -0.03 0.03 -0.01
Unknown 0.00 -0.77 -0.10 1.25 -0.36
Bicycle Yes 58.36 -0.18 0.04 -0.22 -0.02
No 41.64 0.26 -0.04 0.30 0.02
Scooter Yes 27.27 1.32 0.53 -0.10 0.26
No 72.73 -0.49 -0.24 0.04 -0.12
Car Yes 4.47 2.32 0.70 -0.07 0.41
No 95.53 -0.11 -0.05 0.00 -0.03
Clock Yes 87.00 0.20 0.07 0.04 0.05
No 13.00 -1.33 -0.57 -0.24 -0.36
Computer Yes 5.28 2.75 0.87 0.15 0.67
No 94.72 -0.15 -0.06 -0.01 -0.05
Mobile
handsetYes 85.22 0.28 0.12 -0.03 0.08
No 14.78 -1.62 -0.74 0.17 -0.45
Landline
phoneYes 5.53 2.05 0.60 0.33 0.35
No 94.47 -0.12 -0.05 -0.02 -0.03
Electric fan Yes 71.89 0.45 0.14 0.12 0.11
No 28.11 -1.16 -0.39 -0.30 -0.30
Refrigerator Yes 20.93 1.80 0.53 0.04 0.37
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No 79.07 -0.48 -0.19 -0.01 -0.14
Note: The ownership of durable goods and the use of AYUSH treatments is included in theanalysis as supplementary variables. 30.14 percent of Indians live in households thatuse AYUSH treatments. The coordinates of the modalities (Yes/No) of the PCA and of theSFA are presented in the columns.
65 Secondly, we selected 14 social position variables that allow us to grasp different
segmentations of Indian society, mainly the level of consumption, the living environment
(urban or rural, and geographic16), caste, religion and social class. The latter was
constructed using the class schema developed by Vaid (2012), to date the only schema
that offers a synthetic representation of social classes in Indian society.
Table A3 – Supplementary variables (2): Social positions ofhouseholds
ModalityDistribution
(%)
Axis 1 Axis 2
Coord.
PCA
Coord.
SFA
Coord.
PCA
Coord.
SFA
Standard of
living
MPCE
P0-P5 5.00 -2.49 -1.15 -0.06 -0.63
P5-P10 5.00 -1.88 -0.63 -0.40 -0.65
P10-P20 10.00 -1.49 -0.56 -0.32 -0.55
P20-P30 10.00 -1.08 -0.40 -0.26 -0.48
P30-P40 10.00 -0.74 -0.32 -0.21 -0.38
P40-P50 10.00 -0.37 -0.24 -0.03 -0.30
P50-P60 10.00 0.00 -0.13 0.06 -0.18
P60-P70 10.00 0.41 -0.01 0.16 -0.05
P70-P80 10.00 0.94 0.15 0.18 0.13
P80-P90 10.00 1.63 0.36 0.28 0.36
P90-P95 5.00 2.36 0.55 0.35 0.66
P95-P100 5.00 3.41 0.94 0.38 0.97
Ration card
Ration card
holder84.11 -0.10 -0.07 0.04 -0.04
No ration card
hold15.88 0.50 0.29 -0.18 0.17
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Ration card
unknown0.02 0.66 0.80 -1.43 -0.08
Land owned
No land owned 10.51 1.31 0.21 0.77 0.55
0-1 acre land
owned58.05 -0.19 -0.16 0.08 -0.04
1-5 acres land
owned23.45 -0.28 0.10 -0.30 -0.15
5-10 acres land
owned5.29 0.30 0.37 -0.66 -0.09
10-20 acres land
owned2.02 0.71 0.52 -0.73 -0.12
20+ acres land
owned0.67 1.17 0.68 -0.73 -0.01
Social
category
Social class
Farmers-owners
(large and
medium)
6.53 0.35 0.47 -0.78 -0.18
Farmers small
and tenants15.10 -0.42 0.10 -0.50 -0.31
Lower
agriculturists21.95 -1.08 -0.43 0.09 -0.38
Higher
professionals3.69 2.20 0.58 0.27 0.54
Lower
Professionals4.27 1.47 0.49 0.11 0.37
Business 6.77 0.96 0.18 0.16 0.18
Petty business 0.86 -0.33 -0.37 0.13 -0.04
Routine non
Manual7.25 0.80 0.14 0.21 0.19
Lower Routine-
non Manuel2.53 0.67 -0.03 0.31 0.20
Skilled workers 7.95 0.63 -0.05 0.37 0.08
Semi and
unskilled
workers
19.07 -0.44 -0.44 0.09 -0.19
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Occupation
unknown4.03 0.73 0.11 0.26 0.28
Salary
Regular salary
earner20.95 1.23 0.27 0.32 0.34
No regular salary
earner79.04 -0.33 -0.11 -0.09 -0.13
Salary unknown 0.01 0.75 0.48 0.41 0.58
Education
illiterate 33.24 -0.81 -0.42 -0.15 -0.34
literate below
primary12.28 -0.48 -0.24 0.13 -0.23
Primary school 12.60 -0.18 -0.17 0.14 -0.09
Middle school 15.03 0.10 0.00 0.09 -0.04
Secondary and
certificate19.08 0.90 0.32 -0.02 0.24
Graduate and
postgraduate7.76 2.12 0.65 0.08 0.60
Edu unknown 0.01 -0.48 0.00 -0.64 0.57
Reserved
group
(caste)
STs 8.93 -1.13 -0.19 0.47 -0.11
SCs 19.03 -0.55 -0.33 0.01 -0.17
OBCs 44.06 0.00 0.01 -0.07 -0.07
Upper castes 27.97 0.74 0.25 -0.05 0.23
Caste unknown 0.01 -1.37 0.26 -0.01 0.59
Religion
Hinduism 81.49 -0.01 0.01 -0.02 -0.01
Islam 13.64 -0.20 -0.18 0.06 0.03
Christianity 2.23 0.75 0.13 1.21 0.04
Sikhism 1.61 1.07 0.12 -1.31 0.00
Jainism 0.25 2.39 0.69 -0.03 0.76
Buddhism 0.59 -0.15 -0.06 0.57 -0.10
Zoroastrianism 0.00 4.52 1.05 1.46 -1.10
Religion Others 0.19 -0.38 0.19 0.53 -0.15
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Religion
unknown0.00 1.63 1.23 1.02 -0.11
Overlap
Hindu/
reserved
group
Hindu low caste 61.56 -0.33 -0.12 0.01 -0.12
Hindu Upper
castes19.92 0.96 0.33 -0.14 0.27
Non Hindu low
caste10.46 -0.04 -0.05 0.06 -0.04
Non-Hindu
middle & higher
castes
8.04 0.19 0.00 0.16 0.13
Cross Caste
unknown0.01 -1.35 0.27 0.01 0.61
Geographic
environment
State Jammu &
Kashmir0.89 0.28 1.11
Himachal
Pradesh0.59 0.26 0.49
Punjab 2.31 1.00 -1.17
Chandigarh 0.09 1.73 0.03
Uttaranchal 0.86 0.18 0.00
Haryana 2.25 1.15 -1.34
Delhi 1.14 1.63 0.19
Rajasthan 5.54 0.64 -0.86
Uttar Pradesh 16.54 -0.39 -1.17
Bihar 8.47 -0.73 -0.93
Sikkim 0.04 0.82 1.23
Arunachal
Pradesh0.09 -0.28 1.59
Nagaland 0.11 0.91 0.39
Manipur 0.21 0.54 0.11
Mizoram 0.09 -0.40 1.99
Tripura 0.32 -0.83 2.27
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Meghalaya 0.24 0.66 1.81
Assam 2.54 -1.00 1.60
West Bengal 7.68 -0.47 0.17
Jharkhand 2.49 -0.74 -0.66
Orissa 3.35 -1.42 0.36
Chhattisgarh 2.18 -1.75 0.25
Madhya Pradesh 5.92 -0.68 -0.40
Gujarat 5.02 0.49 -0.06
Daman & Diu 0.01 0.82 0.92
D & N Haveli 0.03 -0.15 0.75
Maharashtra 9.50 0.52 0.33
Andhra Pradesh 7.22 0.59 0.92
Karnataka 5.11 0.62 0.81
Goa 0.11 2.05 1.49
Lakshadweep 0.00 0.49 3.25
Kerala 2.84 1.46 1.54
Tamil Nadu 6.10 0.46 2.15
Pondicherry 0.10 1.16 1.44
A & N Islands 0.03 -0.51 3.00
Sector
Rural 71.43 -0.49 -0.09 -0.16 -0.20
Urban 28.57 1.22 0.19 0.39 0.43
Density
(inhab /
km2)
Density rural
very low1.29 -0.54 -0.10 0.47 -0.22
Density rural low 12.59 -0.70 -0.14 0.07 -0.27
Density rural
medium21.03 -0.33 -0.11 0.22 -0.22
Density rural
medium high20.46 -0.51 -0.03 -0.41 -0.20
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Density rural
high15.86 -0.53 -0.06 -0.57 -0.10
Density rural
very high0.19 1.56 0.18 0.39 -0.21
Density urban
very low0.33 1.07 0.33 0.24 0.21
Density urban
low3.02 0.79 0.21 0.27 0.38
Density urban
medium6.94 1.09 0.22 0.46 0.34
Density urban
medium high7.41 1.20 0.11 0.31 0.42
Density urban
high6.21 1.01 0.07 0.21 0.59
Density urban
very high4.67 2.02 0.34 0.73 0.66
Density urban
unknown0.00 1.14 0.13 0.45 -0.06
Density rural
unknown0.00 0.63 0.37 0.04 -0.20
Structure of
the household
Joint family
No joint fam 55.91 -0.06 -0.06 0.15 0.06
Joint family 44.09 0.07 0.11 -0.19 -0.10
Live-in
domestic
staff
No servant 99.50 -0.01 0.00 0.00 0.00
Servant 0.50 1.65 0.57 0.02 0.33
Status of
the
residence
No dwelling unit 0.06 -1.45 -0.39 0.24 -0.32
Hiring dwelling
unit9.61 1.89 0.40 0.78 0.74
Dwelling owner 88.58 -0.20 -0.05 -0.10 -0.10
Other kind of
dwelling unit1.75 -0.06 -0.14 0.66 0.19
Dwelling
unknown0.00 1.92 0.44 0.10 -0.16
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Note: Social position indicators are included in the analysis as supplementary variablesin order to interpret the factorial plans. The share of each modality, along with thecoordinates in the PCA and the SFA are presented here. The coordinates of the differentstates in the SFA are not present since the state variable is used to “neutralize” thefactor plan.
66 The total level of consumption variable by individual MPCE (Monthly Per Capita
Expenditure) provides an approximate indicator of the households’ wealth, in the absence
of reliable data on the income (The NSSO surveys never record the households’ income).
Following the codification used in the survey reports, we obtain a breakdown of the
variable into 12 categories as the MPCE is split into 10 deciles and quintiles are
substituted for the extreme deciles.
67 Using Divya Vaid’s quantitative work cited above, a social class schema was constructed
on the basis of the main profession of the household. As only 22 percent of women
between the ages of 15 and 59 belong to the active population, the social class is primarily
a reflection of the male head of the household’s economic activity. Vaid’s schema was
produced by modifying Goldthorpe’s typology of “occupations” in British society, to
adapt it to Indian society, more specifically by associating rural and urban professions in
the same grid (Table A4). Farmers (over 40 percent of the active population) are thus
divided into three categories, according to their status as day laborers, sharecroppers or
landowners. The breakdown of class by sector of residence also shows that this grid of
analysis is well suited to India. Although certain categories are over-represented in urban
areas and others in rural areas, this schema clearly offers a synthetic view of social
classes in India (Table A5). The construction of this variable, based on the National
Classification of Occupations-2004 nomenclature (designed by the Directorate of General
Training of the Ministry of Skill Development and Entrepreneurship, based on the
ISCO-1988 nomenclature), nonetheless leaves the social class of 4 percent of the
population, located more amongst the richest and most urban fractions of the social
space, in abeyance. Whether it be due to the absence of a profession (rentiers) or the
refusal to declare their economic activity, we can conjecture about this fraction of the
social space that is impossible to grasp. The survey also records whether the profession is
a salaried one or not, which allows us to make a distinction between the self-employed,
day laborers and salaried employees.
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Table A4 – Class schema used by Divya Vaid (Excerpt from Vaid 2005)
Table A5 – Social composition of the rural and urban Indianpopulation by class.
All India Rural Urban
Higher professionals 3.69 1.29 9.7
Lower professionals 4.27 2.81 7.94
Routine non-manual 7.25 4.63 13.81
Lower Routine non-manual 2.53 1.69 4.65
Business 6.77 4 13.67
Petty business 0.86 0.67 1.32
Skilled workers 7.95 5.06 15.18
Semi and unskilled workers 19.07 17.91 21.94
Farmers-owners (large and medium) 6.53 8.64 1.23
Farmers small and tenants 15.1 20.53 1.52
Lower agriculturists 21.95 29.46 3.17
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Occupation unknown 4.03 3.3 5.88
Total 100 100 100
Note: 3.7 percent of the Indian population lives in a household where the mainprofessional activity belongs to the Higher Professionals social class.
68 The social structure of caste is reduced to a study of official categories: Scheduled caste
(SC), Scheduled Tribes (ST), Other Backward Classes (OBC). We further clustered all the
groups that do not benefit from reservations under the label “Forward castes” or “Upper
castes”. A knowledge of the religion of the households also allows us to specify the
congruence between caste and class, as Divya Vaid showed. She thus distinguishes the
Muslim minority (13.5 percent of the estimated population in our survey) from the rest of
the population, and establishes that this category is very close to the SCs and STs. This
justifies our creation of a variable combining Forward caste or low castes (combining ST,
SC and OBC) with the Hindu religion (81 percent of the estimated population) or religious
minority (the non-Hindus are Muslim, 13.5 percent, Christian, 2.3 percent, Sikh,
1.6 percent, Jain 0.25 percent or belong to other religious minorities).
Table A6 – Distribution of the population between Hindu and non-Hindu forward castes and Hindu and non-Hindu lower castes
Modalities Pan-India Rural Urban
Hindu Forward castes 20.0 15.9 29.8
Non Hindu forward castes 8.0 7.1 10.5
Hindu low caste 61.6 67.2 47.5
Non Hindu low caste 10.4 9.8 12.2
TOTAL 100 100 100
Note: 20 percent of the population belongs to the forward Hindu castes.
69 Lastly the household’s geographical location is taken into account through the State or
Union Territory of residence and the urban or rural living environment. But in order to
transcend the latter dichotomy, a variable showing population density by district was
introduced in an attempt to envisage more precise levels of urban development and rural
isolation. This variable was constructed from the 2011 Census data and follows a
classification of 6 density levels per district, distinguishing the urban and the rural. This
provides a variable with 12 modalities (Table A7). Indeed, it is quite surprising to note
that apart from the high and very high density modalities, the rural environment is over-
represented (in comparison to the total proportion of inhabitants in rural environments)
in all the density categories, including for the medium high and medium densities. This
result is yet another argument reinforcing the inadequacy of the categories rural and
urban, and the relevance of the introduction of our variable. The latter is nonetheless to
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be considered cautiously as in itself it does not represent an indicator of urban
development.
Table A7 – Proportion of the population in each density category, byurban and rural Census categories
Rural Urban Total
Density very high 3.8 96.2 100
Density high 71.3 28.7 100
Density medium high 73.4 26.6 100
Density medium 75.2 24.8 100
Density low 80.7 19.3 100
Density very low 79.9 20.1 100
Density unknown 72.5 27.5 100
Total 71.4 28.6 100
Note: 3.8 percent of rural inhabitants live in a very high density zone.
Principal component analysis and standard factor
analysis
70 The next stage consisted of producing a Principal Component Analysis (PCA) that permits
a graphic representation on two-dimensional planes, of the correlations measured
between the budget variables (active variables17), while seeking associations with social
position variables (supplementary variables). The analysis is weighted using the survey
weight, which results from the weighting adjustment of random sampling design by the
NSSO. The use of the weighting coefficient multiplied by the number of people in the
household (a choice in line with other works that use this type of surveys for
consumption budgets) can be equated to studying the consumption of the average person
in each household. Here we only interpret the first plane made up of the two main
dimensions of the PCA (see Figure A1 for the cloud of individuals)—those that best resume
the inertia of the correlations between the active variables (or in other words, provide
the most information, see Table A8 for the variance explained by the different factors).
Table A8 – Eigenvalues and inertia of all factors of PCA
FactorProper
value
Variance
(%)
Cumulative
variance (%)
Modified rate
(%)
Cumulative modified
rate (%)
1 3.42 9.25 9.25 24.10 24.10
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2 2.33 6.29 15.54 11.11 35.21
3 1.83 4.94 20.48 6.83 42.04
4 1.63 4.41 24.90 5.43 47.47
5 1.45 3.93 28.83 4.30 51.77
6 1.33 3.61 32.44 3.61 55.38
7 1.26 3.41 35.85 3.23 58.61
8 1.23 3.31 39.16 3.04 61.65
9 1.14 3.09 42.25 2.64 64.29
10 1.12 3.02 45.27 2.51 66.80
11 1.09 2.94 48.21 2.39 69.19
12 1.04 2.80 51.01 2.17 71.36
13 1.02 2.76 53.77 2.10 73.46
14 1.01 2.73 56.50 2.06 75.52
15 0.98 2.66 59.16 1.94 77.46
16 0.97 2.62 61.77 1.89 79.35
17 0.93 2.51 64.28 1.73 81.08
18 0.91 2.47 66.75 1.68 82.76
19 0.91 2.45 69.20 1.65 84.41
20 0.90 2.44 71.64 1.64 86.05
21 0.86 2.34 73.98 1.50 87.55
22 0.85 2.29 76.27 1.43 88.98
23 0.81 2.20 78.47 1.33 90.31
24 0.80 2.17 80.64 1.29 91.60
25 0.77 2.08 82.72 1.18 92.78
26 0.72 1.95 84.67 1.03 93.81
27 0.71 1.92 86.59 1.00 94.81
28 0.71 1.91 88.50 0.99 95.80
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29 0.65 1.76 90.26 0.84 96.64
30 0.59 1.60 91.86 0.69 97.33
31 0.55 1.49 93.35 0.60 97.93
32 0.55 1.49 94.84 0.59 98.52
33 0.50 1.34 96.18 0.48 99.00
34 0.43 1.17 97.34 0.36 99.36
35 0.40 1.09 98.44 0.31 99.67
36 0.39 1.04 99.48 0.28 99.95
37 0.19 0.52 100.00 0.07 100.00
Note: The first two factors summarize 15.5 percent of the total variance, and35.21 percent when using the modified rate.
Figure A1 – Cloud of individuals of the unstandardized factor analysis of the Indian social space. Plane 1-2
71 The first results on the two selected axes immediately reveal the importance of the
geographic location of the households (although this tendency had already been taken
into consideration in our choice of variables). The “States” variable is the most highly
correlated, by far, not only on the second axis but also on the following axes, while on
axis 1 it is already in second position after the level of consumption. In other words,
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although it is not the first, the State variable “explains” a large share of the correlations
observed.
72 This regional dimension of our results tends to mask other social disparities in the social
space (see the core article, part II, on this point). These potentially hidden associations
are unfortunately those we were initially trying to objectify when we started our project.
We hence run a Standardized Factor Analysis (SFA, see Bry, Robette and Roueff, 2015 for a
complete description) to neutralize geographical variations in the representation of the
social space. The SFA consists of integrating a reasoning of the type: “all other things
being equal” based on regression analyses, into the context of a factor analysis. A first
factor analysis is performed (in our case it corresponds to the PCA presented above) and
the coordinates of the people on each axis selected for the rest of the analysis are
extracted. Then a series of linear regressions is carried out (as many as there are axes
selected, in our case ten) using the people’s coordinates as dependent variables and the
variable(s) to be “neutralized” as (an) independent variable(s). The residues of each
regression are collected to obtain a table of coordinates of individuals on each selected
axis “free” of the effects of the “neutralized” variables (see table A1, A2 and A3). A
principal component analysis based on these new coordinates then makes it possible to
obtain a new factor space, on to which the active variables from the initial analysis are
projected as supplementary variables.
73 Applying SFA to the geometric plane also makes it impossible to use statistics that are
traditionally mobilized in order to assess the contribution of each factor to the dispersion
of individuals (variance of axes and contribution of individuals and categories to the
axes). The analysis hence focuses on the levels of correlation of the active variables to the
axes, which corresponds to the graphic coordinates of the variables. In addition, this
measurement is completed by the quality of the representation of the active variables on
the axes (cos2 measurements, see Table A1). In what concerns the supplementary
variables, we use the v-test, which measures the distance (expressed in standard
deviations of the normal distribution) between the modalities and the center of the axis.
The higher the value, the better the modality characterizes the axis. Above all, the
graphic interpretation is exploited (see Figure A2 and Figure A3). The active variables
close to the origin of both axes (below 0.01) are not essential to the construction of the
geometrical plane, and hence we do not project them on the figure. With regard to the
supplementary social position variables, we represent the modalities with test values that
are among the thirty highest on at least one of the axes. It should be noted that for the
social position variables (and only for these), the variables close to the origin of the axes
are not eliminated, as we consider these variables important for an attempt to
understand the “middle” of the social space. Lastly, the analysis is attentive to the “eta2”
indices which measure the structuring power of the supplementary variables on an axis
(this index calculates the relationship between the variance of the modalities of a variable
and the variance of an axis).
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Figure A2 – Unstandardized factor analysis of the Indian social space. Plane 1-2
Note: In blue: consumption level, other dots: other position variables, crosses: the shareof the budget dedicated to each good; diamonds: expenditure level for each good owned.
74 If our SFA is based on ten factors, our analysis is nonetheless limited to its first two
factors. This is why, in order to attain more robust results, we finally decided to also run
an Ascending Hierarchical Classification (AHC) on the first ten factors of the SFA. As
presented in the core of the article (part III, section 2), this allows to push further the
multidimensional approach of the Indian social space by simultaneously integrating in
the analysis elements that could not be combined when interpreting the first two axes of
the SFA. It is to be noticed here that the results of the AHC conducted on the ten axes are
similar (although not identical) to the classification results obtained when limiting
ourselves to the first two or three factors of the SFA. This comforts our analysis: the first
two factors summarize adequately the variability of the household budget.
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Figure A3 – Cloud of individuals of the standardized factor analysis of the Indian social space. Plane 1-2
Appendix 2: Zooms on the social space
75 The replication of our study on different sub-samples of the NSS “consumption” survey
reveals both a global stability of the results found at the all-India level as well as some
minor—but heuristic—variations. It also confirms the accuracy of a relational analysis
that takes into account all available social properties rather than monetary wealth only.
This appendix presents these results, based on the production of standardized factor
analyses (see Appendix 1) for two sets of subsamples: the urban and the rural households,
and the richest, intermediate, and poorest households.
Variations according to the rural/urban continuum:
more capital in cities
76 As mentioned in the article, the urban is commonly associated with the megapolis and
the rural with the small isolated, agricultural village. Common sense hence tends to
position them as two incommensurable universes. To explore further this rural/urban
divide, we created two subpopulations, one urban (Figure B1) and the other rural
(Figure B2), in order to produce two separate SFAs (or standardized PCAs).
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Note: In red, the consumption level; yellow dots: the other position variables(specifically: level of education, professional category, religion, Scheduled Caste,demographic density, land ownership, area of residence); in blue: proportion of thebudget dedicated to each good; orange dots: the level of expenditure for each good.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
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Note: In red, the consumption level; yellow dots: the other position variables(specifically: level of education, professional category, religion, Scheduled Caste,demographic density, land ownership, area of residence); in blue: proportion of thebudget dedicated to each good; orange dots: the level of expenditure for each good.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
77 To start with, the two spaces are overall very similar to each other and to the global
space. The first impression is that we are dealing with a copy of the global SFA. Indeed
one notices the same diagonal that contrasts the rich, educated and professionals, with
the poor, little educated and subaltern workers, and the possession of comfort goods with
the share of expenditure on subsistence goods. It is only by carefully observing the
contributions of the position variables to axes 1 and 2 that one can see some minor
differences (Table B1).
Table B1 – Rural and Urban SFA: correlations of supplementaryvariables to axes 1 and 2
Urban Rural
Axe 1 Axe 2 Axe 1 Axe 2
Eta² Rural/Urban - - - -
MPCE 32% 19% 30% 7%
Social position 16% 3% 10% 7%
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Land owning 1% 7% 3% 5%
Education level 24% 2% 12% 4%
Caste 4% 0% 3% 3%
Joint family 0% 1% 0% 2%
Hindu or non-Hindu castes 4% 0% 3% 2%
Salaried worker 3% 1% 5% 1%
Ration card 2% 1% 0% 1%
Religion 2% 0% 0% 0%
Servant 0% 0% 0% 0%
Density 0% 2% 0% 0%
Residential status 1% 8% 3% 0%
Note: Eta2 measures the correlation between the axis and the categorical variables. MPCE “explains”32 percent of the variance of axis 1 in urban areas, 30 percent in rural areas.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
78 In urban areas, the relationship to ownership of the residence and land is associated with
axis 2, connected to demographic density: there is an opposition between small
landowners and people who own their homes on the one side and non-landowners and
people who rent their homes on the other side. Counter intuitively, in comparison to
other countries, it is the fact of being an (small) owner that correlates with low levels of
consumption and education, and the fact of being a tenant of one’s residence that is
associated with high levels of expenditure and education. This does not signify that the
majority of poor people own their homes, but rather, it expresses differential
relationships to the contractualization of economic relationships. Often, a corner of a
property or a building is “made available” to the families of those employed by the owner
(or one of his allies), or these spaces are informally rented (without a lease, rent receipts,
etc.), or makeshift housing is tolerated in an area of a commune of uncertain ownership…
Thus, while most poor people’s status in terms of their residence would, in some way, be
“tenant” or “usufructuary”, these legal categories are, in fact, incorrect and do not make
sense to the people concerned (some, who built their accommodation themselves, may
even consider themselves the “owners” of it). Only the richest, less socially distanced
from their owner when they are tenants, declare themselves to be “tenants” (and they
more often draw up contracts). As for small landowners, they are located on the side of
low demographic densities on axis 2 and among the high levels of consumption on axis 1:
the richest owners of their homes, which also have land attached to them, live in
residential areas.
79 Inversely, in rural areas, the occupancy status of the home, and ownership of land are not
associated to the same axis. When we find tenants on the “rich” side, they are associated
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with axis 1. On axis 2, small owners shift to the “poor” side and the modalities of large
owners appear on the “rich” side, associated with farmer owners. The second axis thus
contrasts households that have land capital with industrial or agricultural worker
households that possess no capital other than their labor power and, sometimes, a small
farm—in short the axis contrasts capitalists with their employees. The former are also
(weakly) associated with OBCs, which include a certain number of castes that are
dominant in rural areas, and the latter with SCs and STs, although they are little
differentiated on axis 1. Axis 1, for its part, contrasts levels of consumption and
education, such as those associated or not with a qualified salaried job (this is why lower
professionals are found on the side of the professionals).
80 Thus, in rural and urban areas, the contrasting principles are very similar with,
nonetheless, slight secondary differences. The social structure is globally the same but
tempered by the specificities of urban and rural spaces. This is confirmed by the
distribution of professional categories, levels of consumption and levels of education,
depending on whether the person lives in a rural or urban area (see tables B2, B3 and B4).
Unsurprisingly this distribution reproduces the polarization visible in the global SFA, but
additionally it appears to be strictly linear. The higher is the urban share, the higher the
level of expenditure, the level of education and the professional category (with the
expected exception of farmers who own large and average size farms).
Table B2 – Social position by sector of residence
Note: 25 percent of Higher Professionals live in rural areas.
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Rural Urban Total
Higher professionals 25.0 75.0 100
Lower Professionals 46.9 53.1 100
Routine non Manual 45.6 54.4 100
Lower Routine-non Manuel 47.5 52.5 100
Business 42.3 57.7 100
Petty business 56.1 43.9 100
Skilled workers 45.4 54.6 100
Semi and unskilled workers 67.1 32.9 100
Farmers-owners (large and medium) 94.6 5.4 100
Farmers small and tenants 97.1 2.9 100
Lower agriculturists 95.9 4.1 100
Total 71.4 28.6 100
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
Table B3 – Level of consumption by sector of residence
Note: 94.1 percent of the population belonging to the 5 percent fractile of the lowestlevel of consumption live in rural areas.
Rural Urban Total
MPCE P0-P5 94.1 5.9 100
MPCE P5-P10 91.1 8.9 100
MPCE P10-P20 88.9 11.1 100
MPCE P20-P30 87.2 12.8 100
MPCE P30-P40 84;5 15.5 100
MPCE P40-P50 79.2 20.8 100
MPCE P50-P60 76.9 23.1 100
MPCE P60-P70 69.7 30.3 100
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MCPE P70-P80 60.1 39.9 100
MPCE P80-P90 47.1 52.9 100
MPCE P90-P95 34.2 65.8 100
MPCE P95-P100 22.0 78.0 100
Total 71.4 2.6 100
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
Table B4 – Level of education by sector of residence
Note: 84.7 percent of the population that lives in a household in which the referenceperson is illiterate, lives in a rural area.
Rural Urban Total
Illiterate 84.7 15.3 100
Literate below primary 79.1 20.9 100
Primary school 75.1 24.9 100
Middle school 71.8 28.2 100
Secondary and certificate 56.6 43.4 100
Graduate and postgraduate 32.2 67.8 100
Total 71.4 28.6 100
Data: Consumer Expenditure Survey. National Sample Survey, 68th Round(2011-2012)
Variations according to the wealth (MPCE)
continuum: a scale with many levels
81 Zooming on various subsamples in terms of wealth produces similar results as zooming
on the rural and urban sub-spaces. Whether it is for the 20 percent richest households,
the 50 percent intermediate households, or the 30 percent poorest households, results are
very similar, displaying only minor but heuristic differences.
Is the “middle class” made up of the richest 20 percent?
82 The first lesson taught by zooming on the richest 20 percent subsample of our population
(Figure B3), is that the Hindu and non-Hindu forward castes are associated with higher
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levels of capital. It also reveals that in this area of the Indian social space the Muslims and
SCs are not only less endowed with capital, but also more often urban. This is certainly
because, for these discriminated religious and caste minorities, the escape from dominant
castes’ power is more direct and enduring in rural areas, and the opportunities for social
ascension by enrichment are more likely to exist in cities.
83 The second lesson is that while the graph is very similar to the overall SFA, the second
axis is more obviously constructed around the urbanization rate. The main opposition it
reveals is between the highest urban densities on the one side and the highest rural
densities on the other side (with low urban densities and low rural densities closer to the
origin of the axes). This is something that we do not find in the other spaces where the
dispersion of densities on axis 2 is strictly linear. The lowest fractions of the affluent
social space, are clearly associated with rural areas but they are nevertheless more
urbanized than the rural fractions of the intermediate and popular social spaces. In other
words, affluent classes are better described as distributed among different level of
urbanization (from small cities to megapolis) rather than as split between rural and
urban areas. This is probably linked to the fact that it is very unlikely to be among the top
20 percent in terms of income and live in a rural, scarcely dense area.
84 The third lesson is that we clearly see that major differences contrast the upper class,
who are also highly qualified, and higher professionals, with the other well-to-do people,
who are sometimes little qualified such as semi and unskilled manual workers, or lower
agriculturalists. In other words, some comfort goods (computer, refrigerator, car, etc.)
are not accessible to the less rich of the richest 20 percent. The fact that the opposition
between comfort goods and basic goods also constitutes, for the richest 20 percent, the
main factor of polarization between styles of consumption, is an important indicator both
of the strength of the structure of this opposition across the whole Indian social space,
and of the considerable divergences that exist at the “apex” of this space.
85 It hence becomes difficult to identify the richest 20 percent with a homogeneous “middle
class”: it is not only extremely heterogeneous but also, for the most part, far removed
from the stereotype of the mobile urban executive who frequents luxury boutiques in
large shopping malls.
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Figure B3 – SFA, close-up of the richest 20 percent (deciles 80 to 100). Plane 1-2
Note: In red, the consumption level; yellow dots: the other position variables(specifically: level of education, professional category, religion, Scheduled Caste,demographic density, land ownership, area of residence); in blue: proportion of thebudget dedicated to each good; orange dots: the level of expenditure for each good.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
The “intermediate” 50 percent: the key resource of education
86 In line with previous results, the analysis of the social space of the intermediate levels of
consumption does not reveal significant differences with the results at the pan-Indian
level (Figure B4).
87 The most interesting difference appears when one focuses on levels of education. Among
the richest 20 percent there exists a division between the very highly qualified (graduates
and postgraduates) and the others (including the middle school level). The distinguishing
factor within a population that is overall qualified, is being highly qualified—or inversely,
not having any qualification, hence the significance of all the “low education” modalities
(illiterate, literate below primary and primary school, followed by middle school). In this
sub-space, only three modalities are significantly different from the global space: there is
a contrast between the illiterate on the one side and the (post)graduates, secondary and
certificate levels on the other side. Here, it is the mere fact of having prolonged one’s
education that is distinctive and that draws the most educated towards the “top” of this
intermediate zone.
88 At a wider level, this vast intermediate zone, made up of 50 percent of the population,
does not really show any sociological traits that would distinguish it from the other
spaces studies.
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Figure B4 – SFA, close-up of the intermediate 50 percent (deciles 40 to 80). Plane 1-2
Note: In red, the consumption level; yellow dots: the other position variables(specifically: level of education, professional category, religion, Scheduled Caste,demographic density, land ownership, area of residence); in blue: proportion of thebudget dedicated to each good; orange dots: the level of expenditure for each good.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
The poorest 30 percent: the empire of need
89 By zooming in to take a closer look at the poorest 30 percent (Figure 5), we first note that
the position variables projected as supplementary variables sometimes behave
surprisingly. To start with, the routine non-manual workers are located on the left and all
the other social class variables are on the right, with the professionals being close to the
unskilled or semi-skilled workers (while qualified workers and agricultural workers are
not contributing significantly to the construction of the axis). In a similar way, in terms of
level of education, on the left of the axis, the illiterate are associated with the secondary
level and certificate holders, while the primary school level and uncertified high school
level are on the right, along with the graduates and postgraduates. The caste and religion
variables are also related in a way that differs from what is observed on the other spaces
(but that helps explain the inconstancies in terms of education noticed above). The
opposition observed is indeed not one between forward groups and low-status groups but
one between Hindus of relatively high status (forward Hindus and OBC) and low castes
and religious minorities (whether or not from forward caste). On the right, the Hindu
forward castes are the furthest from the origin of axis 1, the OBCs are closer to the origin,
and on the left we find the castes that are traditionally considered inferior (SCs) with the
Muslim and Buddhist religious minorities (not far from the origin). The non-Hindu
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forward castes and the STs are both significant and located on the origin of axis 1, slightly
to its left. It thus seems that among the poorest populations, the fact of belonging to a
Hindu forward caste or to an “other backward class” distinguishes the person from all the
other caste or religious minority attributes. In this sense one can interpret the apparent
inconsistencies of the professional category and of the level of education as an indication
that the small differences between the poor are more strongly based on caste than on
education (rare) or on the social class (as anyway most of them are workers and
agricultural workers). Among the populations that possess no economic or cultural
capital, the caste one belongs to would hence make an enormous difference in terms of
creating a minimum distance from the weight of need.
90 The subspace of the poorest reveals interesting other minor differences. If at first look
one can observe a contrast between comfort goods and basic goods on axis 1, as well as an
opposition between goods of a more urban character and goods that are more common in
rural areas on axis 2, this impression needs to be tempered. The zone of “rural” goods
only contains the variable “cereals” while some goods that are, in other sub-spaces,
located on the “rural” side are now located on the “urban” one. Pulses thus turn out to
become “urban”. In a similar fashion, “spices” are located on the “poor” side and sugar
on the “rich” side of axis 1, a reversed location compared to what is observed on the
other spaces and subspaces studied. What thus seems to be inconsistent cannot, in reality,
better express the effect of need: what is considered poor people’s goods for the
intermediate classes becomes rich people’s goods for the poorest. In other words, what
constitutes a basic good at a certain level (and thus weighs strongly on the budget of the
poorest) is considered a luxury lower down (and is thus traded-off from the consumption
basket of the poorest of the poorest).
91 The third point is that this sub-space reveals what constitute the cumulative
disadvantages that produce extreme deprivation. Indeed, the position variables,
projected as supplementary variables, are all aligned on axis 1—which thus represents
not only the level of wealth, but the volume of almost all economic, cultural, caste,
professional resources. Even ownership of land and the fact of working as a farmer-
owner, which are more dispersed on the rural/urban axis (2) in other spaces and
subspaces, are here mainly dispersed on axis 1. Furthermore, the only position variable
that makes a slightly significant contribution to axis 2 (4 percent of inertia) is the
possession of a ration card. This means that this axis mainly expresses the relationship to
state aid for the most disadvantaged, which we have already described as being more
accessible in urban areas: at this level of the social space, getting access to state aid is not
a sign of deprivation but rather a sign of integration. In summary, in the south of axis 2,
one finds the absence of ration card, Buddhism, very low urban density, and all of these
variables are associated with the “cereals” variable, that is the most vital and essential
food item. This is hence in the South of this subspace that are located the most dominated
amongst the dominated. Their portrait is one of Dalits converted to Buddhism, who are
excluded from accessing administrative resources (although these are intended for the
poorest), who eat the most basic food and who live in areas devoid of both urban micro-
opportunities as well as rural micro-resources (particularly home production).18
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Figure B4 – SFA, close-up of the poorest 30 percent (deciles 0 to 30). Plane 1-2
Note: In red, the consumption level; yellow dots: the other position variables(specifically: level of education, professional category, religion, Scheduled Caste,demographic density, land ownership, area of residence); in blue: proportion of thebudget dedicated to each good; orange dots: the level of expenditure for each good.
Data: Consumer Expenditure Survey, National Sample Survey 68th Round(2011-2012)
92 In conclusion, these zooms confirm the relevance of taking into account the secondary
differentiating factors that “complicate” the massive effect of the level of consumption.
Beyond a division into two (a two-tier India) or three (the classic upper, middle and lower
classes), it is more heuristic to produce a typology of the Indian social space that at the
same times reveals the dizzying length of the social scale, its twists dependent on the
combination of varying social power relationships (wealth, profession, education, caste)
and the multiplicity of its successive levels. This typology is presented in the core article.
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